The present embodiments relate to computation of blood flow in a vessel of a patient. In particular, a hemodynamic metric is estimated from non-invasive medical imaging data.
To estimate a value for flow, a computer model of the vessel is used. For flow in a particular patient, an anatomical model is fit to imaging data for that patient. Computational fluid dynamics estimates the flow from this patient-specific model. However, this approach relies only on geometrical information available from the medical imaging data.
In other approaches, machine learning is used. Either medical images or geometric models extracted from imaging data populate the training database. Features are extracted from these examples for training. The ground truth blood flow measurements are from the patient or computational fluid dynamics measurements. Machine training is performed to create a classifier able to estimate the blood flow from the input features. Due to reliance of patient-specific information, the machine learning may be limited. The training data should include as many examples as possible, such as hundreds or thousands of examples. Given the broad variability in the patient population, an even greater number of examples should be gathered for training. The availability of such examples is limited. The cost and time to gather sufficient training data is a detriment and outlier conditions are less likely to be accounted for in the machine-learnt classifier.